By Roger Thompson, Professor of Mining Engineering, Curtin University Western Australia School of Mines
Much has been made recently about the potential of big data to transform mining and how to capture, evaluate and share this information with those decision makers that value the data. While the amount of data available from mining operations and equipment is increasing, only a fraction of its full value is currently being extracted and used. For most operations, fleet management systems now record truck fleet load, haul, dump and travel times while on-board systems record engine operating and drive information–and more commonly these days, metrics of the truck response to the road it runs on.
In truck haulage, cycle times can vary as a result of many factors, not the least of which is the road condition itself. While we may see, over time, an increase in cycles times, it’s often harder to explain the source of that increase-especially if and when it is related to road deterioration as opposed to simply the geometrics of the haul itself. One critical measure of truck performance and associated cycle time is based on the effect of road rolling resistance-especially the impact of increased rolling resistance on cycle times and unit costs. But can a measure of this effect be extracted directly from existing ‘big data’ and used to inform road management strategies?
This paper examines the extent to which onboard data can be used to replicate the speed-rimpull-gradeability characteristics of a truck and thus isolate road rolling resistance. Ultimately, it may not be just as simple as ‘today’s rolling resistance is 3%,’ but rather interrogating the data to reveal an incremental change indicator which would flag a more pro-active response to road maintenance, as Haulage & Loading 2017 5 opposed to the reactive responses more typical of current operational strategies.